Intraoral scanning and dental condition identification

ABSTRACT

An intraoral scanner generates 2D images of a dental site and 3D intraoral scans of the dental site. The computing device receives the 2D images of the dental site and the 3D intraoral scans of the dental site from the intraoral scanner, generates a 3D model of the dental site based on the 3D intraoral scans of the dental site, and processes at least one of a) one or more of the 2D images of the dental site, b) one or more of the 3D intraoral scans of the dental site, or c) data from the 3D model of the dental site to identify one or more intraoral areas of interest (AOIs) at the dental site. The computing device determines a dental condition associated with the one or more intraoral AOIs, and determines a manner for scanning the one or more intraoral AOIs.

RELATED APPLICATIONS

This patent application is a continuation of U.S. patent applicationSer. No. 17/135,893, filed Dec. 28, 2020, which is a divisional of U.S.patent application Ser. No. 16/923,990, filed Jul. 8, 2020, now issuedas U.S. Pat. No. 10,898,077, which is a continuation of U.S. patentapplication Ser. No. 16/921,715, filed Jul. 6, 2020, now issued as U.S.Pat. No. 11,363,955, which is a continuation of U.S. patent applicationSer. No. 16/146,870, filed Sep. 28, 2018, now issued as U.S. Pat. No.10,702,164, which is a continuation of U.S. patent application Ser. No.15/874,686, filed Jan. 18, 2018, now issued as U.S. Pat. No. 10,610,106,which is a continuation of U.S. patent application Ser. No. 15/347,620,filed Nov. 9, 2016, now issued as U.S. Pat. No. 10,117,581, which is acontinuation of U.S. patent application Ser. No. 14/705,788, filed May6, 2015, now issued as U.S. Pat. No. 9,510,757, which claims the benefitunder 35 U.S.C. § 119(e) of U.S. Provisional Application No. 61/990,004,filed May 7, 2014, all of which are herein incorporated by reference.

TECHNICAL FIELD

Embodiments of the present invention relate to the field of intraoralscanning and, in particular, to a system and method for improving theresults of intraoral scanning.

BACKGROUND

In prosthodontic procedures designed to implant a dental prosthesis inthe oral cavity, the dental site at which the prosthesis is to beimplanted in many cases should be measured accurately and studiedcarefully, so that a prosthesis such as a crown, denture or bridge, forexample, can be properly designed and dimensioned to fit in place. Agood fit enables mechanical stresses to be properly transmitted betweenthe prosthesis arid the jaw, and to prevent infection of the gums viathe interface between the prosthesis and the dental site, for example.

Some procedures also call for removable prosthetics to be fabricated toreplace one or more missing teeth, such as a partial or full denture, inwhich case the surface contours of the areas where the teeth are missingneed to be reproduced accurately so that the resulting prosthetic fitsover the edentulous region with even pressure on the soft tissues.

In some practices, the dental site is prepared by a dental practitioner,and a positive physical model of the dental site is constructed usingknown methods. Alternatively, the dental site may be scanned to provide3D data of the dental site. In either case, the virtual or real model ofthe dental site is sent to the dental lab, which manufactures theprosthesis based on the model. However, if the model is deficient orundefined in certain areas, or if the preparation was not optimallyconfigured for receiving the prosthesis, the design of the prosthesismay be less than optimal. For example, if the insertion path implied bythe preparation for a closely-fitting coping would result in theprosthesis colliding with adjacent teeth, the coping geometry has to bealtered to avoid the collision, which may result in the coping designbeing less optimal. Further, if the area of the preparation containing afinish line lacks definition, it may not be possible to properlydetermine the finish line and thus the lower edge of the coping may notbe properly designed. Indeed, in some circumstances, the model isrejected and the dental practitioner then re-scans the dental site, orreworks the preparation, so that a suitable prosthesis may be produced.

In orthodontic procedures it can be important to provide a model of oneor both jaws. Where such orthodontic procedures are designed virtually,a virtual model of the oral cavity is also beneficial. Such a virtualmodel may be obtained by scanning the oral cavity directly, or byproducing a physical model of the dentition, and then scanning the modelwith a suitable scanner.

Thus, in both prosthodontic and orthodontic procedures, obtaining athree-dimensional (3D) model of a dental site in the oral cavity is aninitial procedure that is performed. When the 3D model is a virtualmodel, the more complete and accurate the scans of the dental site are,the higher the quality of the virtual model, and thus the greater theability to design an optimal prosthesis or orthodontic treatmentappliance(s).

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings.

FIG. 1 illustrates one embodiment of a system for performing intraoralscanning and generating a virtual three dimensional model of a dentalsite.

FIG. 2A illustrates a flow diagram for a method of determining intraoralareas of interest during an intraoral scan session, in accordance withembodiments of the present invention.

FIG. 2B illustrates a flow diagram for a method of providing indicationsfor intraoral areas of interest, in accordance with embodiments of thepresent invention.

FIG. 3A illustrates a flow diagram for a method of providing dataindications of flawed scan data from an intraoral scan session, inaccordance with embodiments of the present invention.

FIG. 3B illustrates a flow diagram for a method of providing dataindications of intraoral areas of interest, in accordance withembodiments of the present invention.

FIG. 3C illustrates a flow diagram for a method of performing intraoralscanning, in accordance with embodiments of the present invention.

FIG. 4A illustrates a portion of an example dental arch during anintraoral scan session.

FIG. 4B illustrates the example dental arch of FIG. 4A during theintraoral scan session after the generation of further intraoral images.

FIG. 5A illustrates an example dental arch showing intraoral areas ofinterest.

FIG. 5B illustrates an example dental arch showing intraoral areas ofinterest and indicators pointing to the intraoral areas of interest.

FIG. 5C illustrates another example dental arch showing intraoral areasof interest and indicators pointing to the intraoral areas of interest.

FIG. 6 illustrates a screen shot of an intraoral scan application, inaccordance with embodiments of the present invention.

FIG. 7 illustrates a block diagram of an example computing device, inaccordance with embodiments of the present invention.

DETAILED DESCRIPTION

Described herein is a method and apparatus for improving the quality ofscans, such as intraoral scans taken of dental sites for patients.During a scan session, a user (e.g., a dental practitioner) of a scannermay generate multiple different images (also referred to as scans) of adental site, model of a dental site, or other object. The images may bediscrete images (e.g., point-and-shoot images) or frames from a video(e.g., a continuous scan). These images may not capture all of the areasof the dental site and/or there may be areas for which there isconflicting data between images. In embodiments described herein, suchmissing areas and/or conflicting areas may be identified as areas ofinterest. This identification may be performed during a scan session.Accordingly, shortly after the user of the scanner has generated one ormore images, the user may be notified of areas of interest that shouldbe rescanned. The user may then rescan the areas of interest during thescan session. This can facilitate quick and accurate scan sessions.

Additionally, indications or indicators of areas of interest can begenerated during a scan session or after the scan session is complete.These indications may indicate classifications associated with areas ofinterest, a severity of the areas of interest, a size of the areas ofinterest, and additional information. The indications may be visible inviews of the dental site or other scanned object in which the actualareas of interest are hidden. This can ensure that the user will be madeaware of the areas of interest regardless of a current view.

Embodiments described herein are discussed with reference to intraoralscanners, intraoral images, intraoral scan sessions, and so forth.However, it should be understood that embodiments also apply to othertypes of scanners than intraoral scanners. Embodiments may apply to anytype of scanner that takes multiple images and stitches these imagestogether to form a combined image or virtual model. For example,embodiments may apply to desktop model scanners, computed tomography (CTscanners, and so forth. Additionally, it should be understood that theintraoral scanners or other scanners may be used to scan objects otherthan dental sites in an oral cavity. For example, embodiments may applyto scans performed on physical models of a dental site or any otherobject. Accordingly, embodiments describing intraoral images should beunderstood as being generally applicable to any types of imagesgenerated by a scanner, embodiments describing intraoral scan sessionsshould be understood as being applicable to scan sessions for any typeof object, and embodiments describing intraoral scanners should beunderstood as being generally applicable to many types of scanners.

FIG. 1 illustrates one embodiment of a system 100 for performingintraoral scanning and/or generating a virtual three dimensional modelof a dental site. In one embodiment, system 100 carries out one or moreoperations of below described in methods 200, 250, 300, 340 and/or 370.System 100 includes a computing device 105 that may be coupled to ascanner 150 and/or a data store 110.

Computing device 105 may include a processing device, memory, secondarystorage, one or more input devices (e.g., such as a keyboard, mouse,tablet, and so on), one or more output devices (e.g., a display, aprinter, etc.), and/or other hardware components. Computing device 105may be connected to a data store 110 either directly or via a network.The network may be a local area network (LAN), a public wide areanetwork (WAN) (e.g., the Internet), a private WAN (e.g., an intranet),or a combination thereof. The computing device 105 may be integratedinto the scanner 150 in some embodiments to improve performance andmobility.

Data store 110 may be an internal data store, or an external data storethat is connected to computing device 105 directly or via a network.Examples of network data stores include a storage area network (SAN), anetwork attached storage (NAS), and a storage service provided by acloud computing service provider. Data store 110 may include a filesystem, a database, or other data storage arrangement.

In some embodiments, a scanner 150 for obtaining three-dimensional (3D)data of a dental site in a patient's oral cavity is operativelyconnected to the computing device 105. Scanner 150 may include a probe(e.g., a hand held probe) for optically capturing three dimensionalstructures (e.g., by confocal focusing of an array of light beams). Oneexample of such a scanner 150 is the iTero® intraoral digital scannermanufactured by Align Technology, Inc. Other examples of intraoralscanners include the 1M™ True Definition Scanner and the Apollo DIintraoral scanner and CEREC AC intraoral scanner manufactured bySirona®.

The scanner 150 may be used to perform an intraoral scan of a patientsoral cavity. An intraoral scan application 108 running on computingdevice 105 may communicate with the scanner 150 to effectuate theintraoral scan. A result of the intraoral scan may be a sequence ofintraoral images that have been discretely generated (e.g., by pressingon a “generate image” button of the scanner for each image).Alternatively, a result of the intraoral scan may be one or more videosof the patients oral cavity. An operator may start recording the videowith the scanner 150 at a first position in the oral cavity, move thescanner 150 within the oral cavity to a second position while the videois being taken, and then stop recording the video. In some embodiments,recording may start automatically as the scanner identifies eitherteeth. The scanner 150 may transmit the discrete intraoral images orintraoral video (referred to collectively as intraoral image data 135)to the computing device 105. Computing device 105 may store the imagedata 135 in data store 110. Alternatively, scanner 150 may be connectedto another system that stores the image data in data store 110. In suchan embodiment, scanner 150 may not be connected to computing device 105.

According to an example, a user (e.g., a practitioner) may subject apatient to intraoral scanning. In doing so, the user may apply scanner150 to one or more patient intraoral locations. The scanning may bedivided into one or more segments. As an example the segments mayinclude a lower buccal region of the patient, a lower lingual region ofthe patient, a upper buccal region of the patient, an upper lingualregion of the patient, one or more preparation teeth of the patient(e.g., teeth of the patient to which a dental device such as a crown oran orthodontic alignment device will be applied), one or more teethwhich are contacts of preparation teeth (e.g., teeth not themselvessubject to a dental device but which are located next to one or moresuch teeth or which interface with one or more such teeth upon mouthclosure), and/or patient bite (e.g., scanning performed with closure ofthe patient's mouth with scan being directed towards an interface areaof the patient's upper and lower teeth). Via such scanner application,the scanner 150 may provide image data (also referred to as scan data)135 to computing device 105. The image data 135 may include 2D intraoralimages and/or 3D intraoral images. Such images may be provided from thescanner to the computing device 105 in the form of one or more points(e.g., one or more pixels and/or groups of pixels). For instance, thescanner 150 may provide such a 3D image as one or more point clouds.

The manner in which the oral cavity of a patient is to be scanned maydepend on the procedure to be applied thereto. For example, if an upperor lower denture is to be created, then a full scan of the mandibular ormaxillary edentulous arches may be performed. In contrast, if a bridgeis to be created, then just a portion of a total arch may be scannedwhich includes an edentulous region, the neighboring abutment teeth andthe opposing arch and dentition. Thus, the dental practitioner may inputthe identity of a procedure to be performed into intraoral scanapplication 108. For this purpose, the dental practitioner may choosethe procedure from a number of preset options on a drop-down menu or thelike, from icons or via any other suitable graphical input interface.Alternatively, the identity of the procedure may be input in any othersuitable way, for example by means of preset code, notation or any othersuitable manner, intraoral scan application 108 having been suitablyprogrammed to recognize the choice made by the user.

By way of non-limiting example, dental procedures may be broadly dividedinto prosthodontic (restorative) and orthodontic procedures, and thenfurther subdivided into specific forms of these procedures.Additionally, dental procedures may include identification and treatmentof gum disease, sleep apnea, and intraoral conditions. The termprosthodontic procedure refers, inter alia, to any procedure involvingthe oral cavity and directed to the design, manufacture or installationof a dental prosthesis at a dental site within the oral cavity, or areal or virtual model thereof, or directed to the design and preparationof the dental site to receive such a prosthesis. A prosthesis mayinclude any restoration such as crowns, veneers, inlays, onlays, andbridges, for example, and any other artificial partial or completedenture. The term orthodontic procedure refers, inter alia, to anyprocedure involving the oral cavity and directed to the design,manufacture or installation of orthodontic elements at a dental sitewithin the oral cavity, or a real or virtual model thereof, or directedto the design and preparation of the dental site to receive suchorthodontic elements. These elements may be appliances including but notlimited to brackets and wires, retainers, clear aligners, or functionalappliances.

A type of scanner to be used may also be input into intraoral scanapplication 108, typically by a dental practitioner choosing one among aplurality of options. If the scanner 150 that is being used is notrecognizable by intraoral scan application 108, it may nevertheless bepossible to input operating parameters of the scanner thereto instead.For example, the optimal spacing between a head of the scanner andscanned surface can be provided, as well as the capture area (and shapethereof) of the dental surface capable of being scanned at thisdistance. Alternatively, other suitable scanning parameters may beprovided.

Intraoral scan application 108 may identify spatial relationships thatare suitable for scanning the dental site so that complete and accurateimage data may be obtained for the procedure in question. Intraoral scanapplication 108 may establish an optimal manner for scanning a targetarea of the dental site.

Intraoral scan application 108 may identify or determine a scanningprotocol by relating the type of scanner, resolution thereof, capturearea at an optimal spacing between the scanner head and the dentalsurface to the target area, etc, For a point-and-shoot scanning mode,the scanning protocol comprises a series of scanning stations spatiallyassociated with the dental surfaces of the target area. Preferably,overlapping of the images or scans capable of being obtained at adjacentscanning stations is designed into the scanning protocol to enableaccurate image registration, so that intraoral images can be stitchedtogether to provide a composite 3D virtual model. For a continuousscanning mode (video scan), scanning stations may not be identified,instead, a practitioner may activate the scanner and proceed to move thescanner within the oral cavity to capture a video of the target areafrom multiple different viewpoints.

In one embodiment, intraoral scan application 108 includes an area ofinterest (AOI) identifying module 115, a flagging module 118 and a modelgeneration module 125. Alternatively, the operations of one or more ofthe AOI identifying module 115, flagging module 118 and/or modelgeneration module 125 may be combined into a single module and/ordivided into multiple modules.

AOI identifying module 115 is responsible for identifying areas ofinterest (AOIs) from intraoral scan data (e.g., intraoral images) and/orvirtual 3D models generated from intraoral scan data. Such areas ofinterest may include voids (e.g., areas for which scan data is missing),areas of conflict or flawed scan data (e.g., areas for which overlappingsurfaces of multiple intraoral images fail to match), areas indicativeof foreign objects (e.g., studs, bridges, etc.), areas indicative oftooth wear, areas indicative of tooth decay, areas indicative ofreceding gums, unclear gum line, unclear patient bite, unclear marginline (e.g., margin line of one or more preparation teeth), and so forth.An identified void may be a void in a surface of an image. Examples ofsurface conflict include double incisor edge and/or otherphysiologically unlikely tooth edge, and/or bite line shift. The AOIidentifying module 115 may, in identifying an AOI, analyze patient imagedata 135 (e.g., 3D image point clouds) and/or one or more virtual 3Dmodels of the patient alone and/or relative to reference data 138. Theanalysis may involve direct analysis (e.g., pixel-based and/or otherpoint-based analysis), the application of machine learning, and/or theapplication of image recognition. Such reference data 138 may includepast data regarding the at-hand patient (e.g., intraoral images and/orvirtual 3D models), pooled patient data, and/or pedagogical patientdata, some or all of which may be stored in data store 110.

The data regarding the at-hand patient may include X-rays, 2D intraoralimages, 3D intraoral images, 2D models, and/or virtual 3D modelscorresponding to the patient visit during which the scanning occurs. Thedata regarding the at-hand patient may additionally include past X-rays,2D intraoral images, 3D intraoral images, 2D models, and/or virtual 3Dmodels of the patient (e.g., corresponding to past visits of the patientand/or to dental records of the patient).

The pooled patient data may include X-rays, 2D intraoral images, 3Dintraoral images, 2D models, and/or virtual 3D models regarding amultitude of patients. Such a multitude of patients may or may notinclude the at-hand patient. The pooled patient data may be anonymizedand/or employed in compliance with regional medical record privacyregulations (e.g., the Health Insurance Portability and AccountabilityAct (HIPAA)). The pooled patient data may include data corresponding toscanning of the sort discussed herein and/or other data. The pedagogicalpatient data may include X-rays, 2D intraoral images, 3D intraoralimages, 2D models, virtual 3D models, and/or medical illustrations(e.g., medical illustration drawings and/or other images) employed ineducational contexts. The pedagogical patient data may include volunteerdata and/or cadaveric data.

AOI identifying module 115 may analyze patient scan data from later in apatient visit during which the scanning occurs (e.g., one or morelater-in-the-visit 3D image point clouds and/or one or morelater-in-the-visit virtual 3D models of the patient) relative toadditional patient scan data in the form of data from earlier in thatpatient visit (e.g., one or more earlier-in-the-visit 3D image pointclouds and/or one or more earlier-in-the-visit virtual 3D models of thepatient). AOI identifying module 115 may additionally or alternativelyanalyze patient scan data relative to reference data in the form ofdental record data of the patient and/or data of the patient from priorto the patient visit (e.g., one or more prior-to-the-visit 3D imagepoint clouds and/or one or more prior-to-the-visit virtual 3D models ofthe patient). AOI identifying module 115 may additionally oralternatively analyze patient scan data relative to pooled patient dataand/or pedagogical patient data.

In an example, AOI identifying module 115 may generate a first virtualmodel of a dental site based on a first scan session of the dental sitetaken at a first time and later generate a second virtual model of thedental site based on a second scan session of the dental site taken at asecond time. The AOI identifying module 115 may then compare the firstvirtual model to the second virtual model to determine a change in thedental site and identify an AOI to represent the change.

Identifying of areas of interest concerning missing and/or flawed scandata may involve the AOI identifying module 115 performing directanalysis, for instance determining one or more pixels or other points tobe missing from patient scan data and/or one or more virtual 3D modelsof the patient. Identification of areas of interest concerning missingand/or flawed scan data may additionally or alternatively involveemploying pooled patient data and/or pedagogical patient data toascertain patient scan data and/or virtual 3D models as being incomplete(e.g., possessing discontinuities) relative to that which is indicatedby the pooled patient data and/or pedagogical patient data.

Flagging module 118 is responsible for determining how to present and/orcall out the identified areas of interest. Flagging module 118 mayprovide indications or indicators regarding scan assistance, diagnosticassistance, and/or foreign object recognition assistance. Areas ofinterest may be determined, and indicators of the areas of interest maybe provided, during and/or after an intraoral scan session. Suchindications may be provided prior to and/or without construction of anintraoral virtual 3D model. Alternatively, indications may be providedafter constructions of an intraoral virtual 3D model of a dental site.

Examples of the flagging module 118 providing indications regarding scanassistance, diagnostic assistance, and/or foreign object recognitionassistance will now be discussed. The flagging module 118 may providethe indications during and/or after an intraoral scan session. Theindications may be presented (e.g., via a user interface) to a user(e.g., a practitioner) in connection with and/or apart from one or moredepictions of teeth and/or gingivae of a patient (e.g., in connectionwith one or more X-rays, 2D intraoral images, 3D intraoral images, 2Dmodels, and/or virtual 3D models of the patient). Indicationpresentation in connection with depictions of patient teeth and/orgingivae may involve the indications being placed so as to correlate anindication with the corresponding portion of the teeth and/or gingivae.As an illustration, a diagnostic assistance indication regarding abroken tooth might be placed so as to identify the broken tooth.

The indications may be provided in the form of flags, markings,contours, text, images, and/or sounds (e.g., in the form of speech).Such a contour may be placed (e.g., via contour fitting) so as to followan extant tooth contour and/or gingival contour. As an illustration, acontour corresponding to a tooth wear diagnostic assistance indicationmay be placed so as to follow a contour of the worn tooth. Such acontour may be placed (e.g., via contour extrapolation) with respect toa missing tooth contour and/or gingival contour so as to follow aprojected path of the missing contour. As an illustration, a contourcorresponding to missing tooth scan data may be placed so as to followthe projected path of the tooth portion which is missing, or a contourcorresponding to missing gingival scan data may be placed so as tofollow the projected path of the gingival portion which is missing.

In presenting indications (e.g., flags), the flagging module 118 mayperform one or more operations to pursue proper indication display. Forinstance, where indications are displayed in connection with one or moredepictions of teeth and/or gingivae (e.g., a corresponding virtual 3Dmodel), such operations may act to display a single indication ratherthan, say, multiple indications for a single AOI. Additionally,processing logic may select a location in 3D space for indicationplacement.

Where indications are displayed in connection with a 3D teeth and/orgingiva depiction (e.g., in connection with a virtual 3D model), theflagging module 118 may divide the 3D space into cubes (e.g., voxelscorresponding to one or more pixels of the 3D space). The flaggingmodule 118 may then consider the voxels relative to the voxels ofdetermined AOIs and tag voxels so as to indicate the indications (e.g.,flags) to which they correspond.

As an illustration, suppose that two indications are to be brought tothe attention of the user via flagging: a first indication which regardsmissing scan data and a second indication which regards a caries. Withregard to the missing scan data indication, the flagging module 118 mayconsider the pixels which correspond to the missing scan data relativeto the cubes and tag each cube which encompasses one or more of thosepixels. The flagging module 118 may perform likewise with regard to thecaries indication.

Where more than one cube is tagged with respect to a given one of theindications, the flagging module 118 may act such that only one of thetagged voxel receives flag placement. Moreover, the flagging module 118may choose a particular voxel which it determines will provide for easeof viewing by the user. For instance, such choice of a voxel may takeinto account the totality of indications to be flagged and may endeavorto avoid crowding a single cube with multiple flags where such can beavoided.

In placing indications (e.g., flags) the flagging module 118 may or maynot take into account factors other than seeking to avoid crowding. Forinstance, the flagging module 118 may take into account availablelighting, available angle, available zoom, available axes of rotation,and/or other factors corresponding to user viewing of the teeth and/orgingiva depiction (e.g., the virtual 3D model), and may seek indication(e.g., flag) placement which seeks to optimize user viewing in view ofthese factors).

The flagging module 118 may key the indications (e.g., via color,symbol, icon, size, text, and/or number). The keying of an indicationmay serve to convey information about that indication. The conveyedinformation may include classification of an AOI, a size of an AOIand/or an importance rank of an AOI. Accordingly, different flags orindicators may be used to identify different types of AOIs. For example,pink indicators may be used to indicate gingival recession and whiteindicators may be used to indicate tooth wear. Flagging module 118 maydetermine a classification, size and/or importance rank of an AOI, andmay then determine a color, symbol, icon, text, etc. for an indicator ofthat AOI based on the classification, size and/or importance rank.

Turning to keying which conveys indication size, the processing logicmay, in implementing such size-oriented keying, employ one or more sizethresholds. The origin of the thresholds may be set (e.g., by a dentalexpert) during a configuration operation and/or may be preset. Theorigin of the thresholds may be set by processing logic which accessespooled patient data and/or pedagogical patient data correlating sizeinformation for foreseeable indications regarding scan assistance (e.g.,information regarding sizes of oral-anatomical portions not imaged orpoorly imaged due to missing and/or flawed scan data) and degree ofsuccess of procedure outcome (e.g., degree of success in orthoticalignment device construction and/or orthotic alignment device patientfit). Larger size may be indicative of greater clinical importance. Forexample, a large void may impair manufacture of an accurate orthodonticaligner, while a large void may not. As an illustration, threethresholds might be set with respect to areas of missing data and/orcaries. Implementation may be such that indications falling into thelargest of the three size thresholds are keyed red and/or with thenumeral “1,” that indications falling into the smallest of the threesize thresholds are keyed purple and/or with the numeral “3,” and/orthat indications falling into the middle-sized of the three thresholdsare keyed yellow and/or with the numeral “2.”

Turning to keying which conveys AOI classification, indicators mayidentify classifications assigned to intraoral areas of interest. Forexamples, AOIs may be classified as voids, changes, conflicts, foreignobjects, or other types of AOI. AOIs representing changes in patientdentition may represent tooth decay, receding gums, tooth wear, a brokentooth, gum disease, gum color, moles, lesions, tooth shade, tooth color,an improvement in orthodontic alignment, degradation in orthodonticalignment, and so on. Different criteria may be used for identifyingeach such class of AOI. For example, a void may be identified by lack ofimage data, a conflict may be identified by conflicting surfaces inimage data, changes may be identified based on differences in imagedata, and so on.

In an example of a surface conflict AOI, a first bite line component maycorrespond to one portion of a patient's teeth (e.g., the upper jaw orto the right side of the jaw). A second bite line component maycorrespond to another portion of the patient's teeth (e.g., the lowerjaw or to the left side of the jaw). The AOI identifying module 115 maycompare the first bite line component to the second bite line componentto check for a deviation. Such a deviation might be suggestive of thepatient having moved his jaw during scanning (e.g., the patient havingmoved his jaw in an interim between a practitioner's scanning of thelower jaw and the practitioner's scanning of the upper jaw, or in aninterim between the practitioner's scanning of the left side of the jawand the and the practitioner's scanning of right side of the jaw).

In performing a bite line shift surface conflict operation, the AOIidentifying module 115 may or may not take into account a deviationthreshold (e.g., set during a configuration operation). The flaggingmodule 118 may or may not then provide indication thereof in the casewhere the found deviation meets the threshold, and not provideindication otherwise. The intraoral scan application 108 may or may notapply corrective measures (e.g., averaging) to such found deviationswhich do not meet the threshold. Where such a threshold is not takeninto account, the flagging module 118 may provide indication of allfound deviations. Although the foregoing is, to facilitate discussion,cast in terms of bite line shift surface conflict, analogous operationsmay be performed, for instance, with regard to other surface conflictindications.

Keying may also include importance rank, which is discussed in greaterdetail with reference to FIG. 3B.

When a scan session is complete (e.g., all images for a dental site havebeen captured), model generation module 125 may generate a virtual 3Dmodel of the scanned dental site. AOI identifying module 115 and/orflagging module 118 may perform operations to identify AOIs and/or toindicate such AOIs before or after a virtual 3D model has beengenerated.

To generate the virtual model, model generation module 125 may register(i.e., “stitch” together) the intraoral images generated from theintraoral scan session. In one embodiment, performing image registrationincludes capturing 3D data of various points of a surface in multipleimages (views from a camera), and registering the images by computingtransformations between the images. The images may then be integratedinto a common reference frame by applying appropriate transformations topoints of each registered image. In one embodiment, processing logicperforms image registration in a manner discussed in patent applicationU.S. Pat. No. 6,542,249, filed Jul. 20, 1999, which is incorporatedherein by reference.

In one embodiment, image registration is performed for each pair ofadjacent or overlapping intraoral images (e.g., each successive frame ofan intraoral video). Image registration algorithms are carried out toregister two adjacent intraoral images, which essentially involvesdetermination of the transformations which align one image with theother. Each registration between a pair of images may be accurate towithin 10-15 microns. Image registration may involve identifyingmultiple points in each image (e.g., point clouds) of an image pair,surface fitting to the points of each image, and using local searchesaround points to match points of the two adjacent images. For example,model generation module 125 may match points of one image with theclosest points interpolated on the surface of the other image, anditeratively minimize the distance between matched points. Modelgeneration module 125 may also find the best match of curvature featuresat points of one image with curvature features at points interpolated onthe surface of the other image, without iteration. Model generationmodule 125 may also find the best match of spin-image point features atpoints of one image with spin-image point features at pointsinterpolated on the surface of the other image, without iteration. Othertechniques that may be used for image registration include those basedon determining point-to-point correspondences using other features andminimization of point-to-surface distances, for example. Other imageregistration techniques may also be used.

Many image registration algorithms perform the fitting of a surface tothe points in adjacent images, which can be done in numerous ways.Parametric surfaces such as Bezier and B-Spline surfaces are mostcommon, although others may be used. A single surface patch may be fitto all points of an image, or alternatively, separate surface patchesmay be fit to any number of a subset of points of the image. Separatesurface patches may be fit to have common boundaries or they may be fitto overlap. Surfaces or surface patches may be fit to interpolatemultiple points by using a control-point net having the same number ofpoints as a grid of points being fit, or the surface may approximate thepoints by using a control-point net which has fewer number of controlpoints than the grid of points being fit. Various matching techniquesmay also be employed by the image registration algorithms.

In one embodiment, model generation module 125 may determine a pointmatch between images, which may take the form of a two dimensional (2D)curvature array. A local search for a matching point feature in acorresponding surface patch of an adjacent image is carried out bycomputing features at points sampled in a region surrounding theparametrically similar point. Once corresponding point sets aredetermined between surface patches of the two images, determination ofthe transformation between the two sets of corresponding points in twocoordnate frames can be solved. Essentially, an image registrationalgorithm may compute a transformation between two adjacent images thatwill minimize the distances between points on one surface, and theclosest points to them found in the interpolated region on the otherimage surface used as a reference.

Model generation module 125 repeats image registration for all adjacentimage pairs of a sequence of intraoral images to obtain a transformationbetween each pair of images, to register each image with the previousone. Model generation module 125 then integrates all images into asingle virtual 3D model by applying the appropriate determinedtransformations to each of the images. Each transformation may includerotations about one to three axes arid translations within one to threeplanes.

In one embodiment, intraoral scan application 108 includes a trainingmodule 120. The training module 120 may provide a user (e.g., apractitioner) with training guidance as to scanning technique, and/ormay highlight scan assistance indications of the sort discussedhereinabove (e.g., ones corresponding to missing and/or flawed scandata) which have occurred in the past and/or have been reoccurring forthat user.

The training module 120 may consider, relative to a training guidancedata pool, scan data (e.g., 3D image point clouds) and/or one or morevirtual 3D models arising from scanning performed by that user which ledto scan assistance indications. The training guidance data pool mayinclude, with respect to the scanning performances of multiple users(e.g., multiple practitioners), scan data and/or one or more virtual 3Dmodels (e.g., one which led to scan assistance indications) along withinformation describing scanning technique changes which might haveprevented and/or mitigated the circumstances which led to the scanassistance indications. The scan data and/or one or more virtual 3Dmodels of the training guidance data pool may be anonymized and/oremployed in compliance with regional medical record privacy regulations.The training module 120 may match the scan data and/or one or morevirtual 3D models arising from scanning performed by the user to scandata and/or virtual 3D models of the training guidance data pool, accesscorresponding information describing scanning technique changes, andpresent such scanning change technique information to the user (e.g.,via a user interface).

As an illustration, the training guidance data pool might, for scan dataand/or one or more virtual 3D models which led to double incisor edgescan assistance indications such as ones corresponding to particularangles of scanning, include information indicating that having performedscanning with a specified angular change might have been preventativeand/or mitigating. For instance, such data might, for scan data and/orone or more virtual 3D models which led to double incisor edge scanassistance indications in a fashion indicative of scanning at a 35degree-to-surface angle—instead of a desired 45 degree-to-surfaceangle—include information indicating that an angular increase of tendegrees-to-surface might be preventative and/or curative. Moreover, suchdata might for scan data and/or one or more virtual 3D models which ledto double incisor edge scan assistance indications in a fashionindicative of scanning with a 40 degree-to-surface angle—instead of thedesired 45 degree-to-surface angle—include information indicating thatan angular increase of five degrees-to-surface might be preventativeand/or curative.

As another illustration, the training guidance data pool might, for scandata and/or one or more virtual 3D models which led to missing and/orflawed scan data scan assistance indications (e.g., ones correspondingto particular geometrical areas, width-height dimensions,width-to-height or other dimensional relationships, and/or orallocations), include information indicating that having performedscanning at one or more specified speeds, cadences, angles, and/ordistances-from-surface might have been preventative and/or mitigating.

The training module 120 may with respect to particular users (e.g.,practitioners) keep historical record (e.g., according to useridentifier) of scan assistance indications over time. The trainingmodule 120 may employ this historical record to highlight scanassistance indications which have occurred in the past and/or have beenreoccurring for a particular user, to identify improvements and/ordeclines in user scanning technique over time, and/or to providescanning technique training guidance which takes into account multiplescanning performances of the user. The training module 120 may or maynot consider the noted training guidance data pool informationdescribing scanning technique changes which may be preventative and/ormitigating.

As one illustration, the training module 120 may in providing indication(e.g., flagging) regarding missing and/or flawed scan data recognizethat a particular user has received same and or similar indication inthe past. For instance, the training module 120 may ascertain that theuser has received missing and/or flawed scan data at a given locationmultiple times, and/or has received missing and/or flawed scan data ofsimilar tenor multiple times (e.g., although at differing locations, theuser has repeatedly received indication reelecting double incisor edgessuggestive of scanning at other than a 45-degrees-from-surface angle).Where the training module 120 so finds an at-hand indication to be onefor which same and/or similar indication has been received in the past,the training module 120 may act to highlight the indication (e.g., via aparticular color).

As another illustration, with respect to a particular user and doubleincisor edge scan assistance indications, the training module 120 may,by consideration of such historical record and such training guidancedata pool scanning technique change information, ascertain that theuser's scanning technique is changing in such a fashion that theemployed scanning is not yet the called-for 45 degrees-to-surface, butthat the employed scanning angle is becoming over time closer to 45degrees-to-surface. In so doing the training module 120 may performmatching with training guidance data pool information in the vein of thenoted differing degrees-to-surface scanning angles leading to doubleincisor edge scan assistance indication (e.g., matching older user datato pool data regarding 60-degree-to-surface scanning angle but morerecent user data to pool data regarding 40-degree-to-surface scanningangle).

FIGS. 2A-3C illustrate flow diagrams of methods for performing intraoralscans of dental sites for patients. These methods may be performed byprocessing logic that comprises hardware (e.g., circuitry, dedicatedlogic, programmable logic, microcode, etc.), software (such asinstructions run on a processing device), or a combination thereof. Inone embodiment, processing logic corresponds to computing device 105 ofFIG. 1 (e.g., to a computing device 105 executing an intraoral scanapplication 108).

FIG. 2A illustrates a flow diagram for a method 200 of determiningintraoral areas of interest during an intraoral scan session, inaccordance with embodiments of the present invention. At block 205 ofmethod 200, an intraoral scan session of a dental site is started by adental practitioner. The scan session may be for an intraoral scan of apartial or full mandibular or maxillary arch, or a partial or full scanof both arches. The dental practitioner may move an intraoral scanner toa first intraoral position and generate a first intraoral image. Atblock 210, processing logic receives the first intraoral image. Thefirst intraoral image may be a discrete image (e.g., taken from apoint-and-shoot mode) or a frame of an intraoral video (e.g., taken in acontinuous scanning or video mode). The intraoral image may be a threedimensional (3D) image having a particular height, width and depth. Insome embodiments, an intraoral scanner is used that generates 3D imageshaving a depth of 12-14 mm, a height of 13-15 mm and a width of 17-19 mm(e.g., a depth of 13 mm, height of 14 mm and width of 18 mm in oneparticular embodiment).

At block 215, processing logic identifies one or more candidateintraoral areas of interest from the first intraoral image. In oneembodiment, a candidate intraoral area of interest is identified byprocessing the intraoral image to identify voxels in the intraoral imagethat satisfy one or more criteria. Different criteria may be used toidentify different classes of intraoral areas of interest. In oneembodiment, the a of missing image data is used to identify AOIs thatmight be voids. For example, voxels at areas that were not captured bythe intraoral image may be identified.

Processing logic may then determine one or more subsets of theidentified voxels that are proximate to one another. Two voxels may beconsidered to be proximate to one another if they are within a thresholddistance from one another. In one embodiment, two voxels are proximateif they are adjacent to one another. All of the voxels in a determinedsubset (e.g., all of the voxels that are connected directly or via otheridentified voxels) are grouped into a volume that makes up the candidatearea of interest. One or multiple candidate areas of interest may beidentified. If the criterion used to identify voxels is a missing data,then the candidate intraoral area of interest may represent a void.Other criteria may be used to identify other classes of AOI.

After the dental practitioner generates the first intraoral image, he orshe moves the intraoral scanner to a second position and generates anext intraoral image. At block 220, processing logic receives the nextintraoral image. At block 230, processing logic compares the secondintraoral image to the first intraoral image. To compare intraoralimages, processing logic determines an alignment between the intraoralimages based on geometric features shared by these intraoral images.Determining the alignment may include performing transformations and/orrotations on one or both of the intraoral images and/or register theintraoral areas of interest to each other. The aligned images may thenbe displayed by the processing logic. Processing logic may also comparethe first intraoral image to a corresponding intraoral image takenduring a prior scan session. This may identify areas of interest such astooth wear, cavities, and so forth.

At block 232, processing logic determines whether any new candidateintraoral areas of interest are present based on the next intraoralimage. At block 235, processing logic determines whether the candidateintraoral areas of interest from the first intraoral image are verifiedas intraoral areas of interest. Such verification may be performed bytesting the proximity and/or geometric conditions of the AOI relative toa surface of the latest intraoral image. In one embodiment, candidateintraoral areas of interest from an intraoral image are dismissed ifthey correspond to a surface (e.g., of a dental site) from anotherintraoral image. Alternatively, if a candidate intraoral area ofinterest does not correspond to a region of a surface from anotherintraoral image, then the candidate intraoral image may be verified asan actual intraoral area of interest. Accordingly, the second intraoralimage may be used to confirm or dismiss candidate intraoral areas ofinterest from the first intraoral image. If a portion of a candidateintraoral area of interest from the first intraoral image corresponds to(e.g., lines up with) a portion of a surface from the second intraoralimage, then the shape and/or size of the candidate intraoral area ofinterest may be modified. If none of the candidate intraoral areas ofinterest are verified as intraoral areas of interest (e.g., if asubsequent intraoral image provides image data for a candidate intraoralarea of interest), the method proceeds to block 245. Otherwise, themethod continues to block 240.

At block 240, processing logic provides an indication of the one or moreverified intraoral areas of interest. In one embodiment, processinglogic interpolates a shape for the intraoral area of interest based ongeometric features surrounding the intraoral area of interest and/orbased on geometric features of the intraoral area of interest (if suchfeatures exist). For example, if the intraoral area of interest is avoid, then the regions around the void may be used to interpolate asurface shape of the void. The shape of the intraoral area of interestmay be displayed in manner to contrast the intraoral area of interestfrom surrounding imagery. For example, teeth may be shown in white,while the intraoral area of interest may be shown in red, black, blue,green, or another color. Additionally or alternatively, an indicatorsuch as a flag may be used as an indication of the intraoral area ofinterest. The indicator may be remote from the intraoral area ofinterest but include a pointer to the intraoral area of interest. Theintraoral area of interest may be hidden or occluded in many views ofthe dental site. However, the indicator may be visible in all or manysuch views. For example, the indicator may be visible in all views ofthe scanned dental site unless the indicator is disabled. The providedindications of the intraoral areas of interest may be displayed whilethe intraoral scan session is ongoing.

At block 245, processing logic determines whether the intraoral scansession is complete. If so, the method continues to block 248. Ifadditional intraoral images are to be taken and processed, the methodreturns to block 220.

At block 248, a virtual 3D model of the dental site is generated. Thevirtual model 3D may be generated as discussed above. The virtual 3Dmodel may be a virtual or digital model showing the surface features ofthe target area. For a virtual 3D model of a full dental arch, the archwidth of the virtual 3D model may be accurate to within 200 microns ofthe arch width of the patient's actual dental arch.

FIG. 2B illustrates a flow diagram for a method 250 of providingindications for intraoral areas of interest, in accordance withembodiments of the present invention. The indications may be providedduring an intraoral scan session (e.g., before generation of a virtualmodel of a dental site) or after an intraoral scan session is complete(e.g., based on a virtual model of the dental site).

At block 255, intraoral images of a dental site are received. Theintraoral images may be received from an intraoral scanner, from a datastore, from another computing device, or from another source. Theintraoral images may be from a single intraoral scan session or frommultiple intraoral scan sessions. Additionally, or alternatively, one ormore virtual models of a dental site may be received. The virtual modelsmay have been computed based on intraoral images from past intraoralscan sessions.

At block 260, processing logic identifies one or more voxels from theintraoral images and/or the virtual models that satisfy a criterion. Thecriterion may be missing data, conflicting data, or data havingparticular characteristics. In one embodiment, the intraoral images arefirst used to compute a virtual model, and the voxels are identifiedfrom the computed virtual model. In another embodiment, the voxels areidentified from individual intraoral images.

At block 265, one or more subsets of the identified voxels that are inclose proximity to one another are identified. At block 270, thesesubsets are grouped into candidate intraoral areas of interest.

At block 275, processing logic determines whether the candidateintraoral areas of interest are verified as intraoral areas of interest.If any candidate intraoral areas of interest are verified as actualintraoral areas of interest, the method continues to block 280.Otherwise, the method proceeds to block 290.

At block 280, classifications are determined for the intraoral areas ofinterest. For example, AOIs may be classified as voids, conflictingsurfaces, changes in a dental site, foreign objects, and so forth.

At block 285, processing logic provides indications of the intraoralareas of interest. The indications may include information identifyingthe determined classifications of the intraoral areas of interest. Forexample, an indicator may identify an intraoral area of interest asrepresenting a void or insufficient image data. Another indicator mayidentify an intraoral area of interest as representing a region forwhich there are conflicting surfaces from different images.

In one embodiment, the indications include flags that are remote fromthe intraoral areas of interest and that point to or otherwise direct aviewer's attention to the intraoral areas of interest. The indicationsmay be visible from views of the dental site at which the actualintraoral areas of interest are hidden. At block 290, the dental site isdisplayed along with any of the indications for the intraoral areas ofinterest.

FIG. 3A illustrates a flow diagram for a method 300 of formulating scanassistance indications concerning missing and/or flawed scan data inaccordance with examples of the present invention. According to a firstaspect, at block 305 of method 300 the processing logic may receive scandata from an intraoral scanner. At block 310 the processing logic, mayperform direct 3D point cloud analysis and/or direct virtual 3D modelanalysis. At block 315 the processing logic may determine one or morepixels and/or other points to be missing from patient scan data and/orfrom one or more patient virtual 3D models. At block 330, the processinglogic may formulate one or more corresponding indications concerningmissing and/or flawed scan data.

According to a second aspect of FIG. 3 , at block 305 the processinglogic may likewise receive scan data from the scanner. At block 320, theprocessing logic may consider patient scan data and/or one or morepatient virtual 3D models relative to entities indicated by pooledpatient data and/or by pedagogical patient data to constitute completeand/or un-flawed data.

At block 325, the processing logic may ascertain the patient scan dataand/or one or more patient virtual 3D models to be incomplete. At block330, the processing logic may likewise formulate one or morecorresponding indications concerning missing and/or flawed scan data.The indications regarding diagnostic assistance provided by theprocessing logic may include indications concerning tooth occlusioncontacts, bite relation, tooth breakage, tooth wear, gingival swelling,gingival recess, and/or caries. To facilitate understanding, examples ofprocessing logic operations performed in connection with providingdiagnostic assistance indications will now be discussed.

FIG. 3B illustrates a flow diagram for a method 340 of performing keyingand display which conveys indication importance rank for intraoral areasof interest in accordance with that which is discussed hereinabove andin connection with examples of the present invention. Processing logicmay assign an importance rank to an indication via a process in whichthe processing logic considers the indication in view of one or morepatient case details and/or one or more rank-altering weighting factors.It is noted that one or more of the rank-altering weighting factorsthemselves may or may not regard patient case details. Such patient casedetails may include a procedure being performed (e.g., preparation forapplication of a crown, preparation for application of an orthodonticalignment device, treatment of suspected caries and/or treatment ofgingival swelling), patient age, patient gender, one or morepreviously-performed procedures (e.g., that a patient's last visit wasto address a crown affected by marginal leakage), and/or patient dentalrecords.

At block 345 of method 340 the processing logic may apply one or moreweighting factors to each of the one or more under-considerationindications. A weighting factor may set forth one or more particularproperties and indicate one or more rank alterations to be performedwhere such properties are met. The rank alterations may includeincreasing an indication's rank by a given value, decreasing anindication's rank by a given value, specifying that an indication beconsidered to possess a zenith rank, and/or that an indication beconsidered to possess a nadir rank. With respect to a given indication,the processing logic may commence by assigning a particular start rankvalue (e.g., zero) to the indication. The processing logic may thenconsider the one or more weighting factors. Having applied thoseweighting factors, the processing logic may ascertain a finalizedimportance rank for the indication. The processing logic may considerthe finalized importance rank for the indication relative to one or moreother indications for which it has performed like operations.

The origin of the rank-altering weighting factors considered by theprocessing logic may be set by processing logic which accesses pooledpatient data and/or pedagogical patient data which includes correlationsbetween foreseeable indications regarding diagnostic assistance (e.g.,tooth wear and/or caries) and importance (e.g., the data might set forthimportance information regarding each of tooth wear and caries thatconveys that caries are of greater import than tooth wear).

Processing logic may set the rank-altering weighting factors such thatmissing and/or flawed scan data corresponding to a portion of teethand/or gingivae that is larger than or equal to a certain size has anincreased rank. Rank-altering weighting factors such as missing and/orflawed scan data which corresponds to a portion of teeth and/or gingivaewhich has certain dimensional characteristics (e.g., having widthmagnitude being greater than or equal to height magnitude, acircumstance that might be viewed as being short and wide or squareshaped) are assigned the zenith rank or more highly ranked. Missingand/or flawed scan data which corresponds to a portion of teeth and/orgingivae which has other dimensional characteristics (e.g., having widthmagnitude being less than height magnitude, a circumstance that might beviewed as being long and narrow) may be assigned the nadir rank or morelowly ranked.

The origin of the rank-altering weighting factors considered by theprocessing logic may be set by processing logic which accesses pooledpatient data and/or pedagogical patient data which includes correlationsbetween foreseeable indications regarding foreign object recognitionassistance (e.g., concerning fillings and/or implants) and importance(e.g., the data might set forth importance information regarding each offillings and implants that conveys that fillings are of greater importthan implants). By consideration of such data-provided correlations—bethey ones regarding scan assistance, diagnostic assistance, or foreignobject recognition assistance—the processing logic may draw conclusionswhich it employs in setting rank-altering weighting factors.

The performed setting—be it done during a configuration operation or byprocessing logic—may provide for one or more weighting factors directedtowards foreseeable indications. One such weighting factor may specifythat an indication relating to the vicinity of (e.g., to theinterproximal areas of) one or more preparation teeth have its rankraised by a specified value. Another such weighting factor may specifythat an indication regarding insufficient preparation tooth margin lineclarity have its rank raised by a specified value or that suchindication should possess a zenith rank. Yet another weighting factormay specify that an indication regarding unclear bite have its rankraised by a specified value. A further weighting factor may specify thatan indication regarding bite line shift have its rank raised by aspecified value. Another weighting factor may specify that an indicationregarding double incisor edge have its rank raised by a specified value.Yet another weighting factor may specify that an indication regarding alack of gum line clarity have its rank raised by a first specified valuein the case where the at-hand procedure does not concern gingivalrecess, but have its rank raised by a second specified value in the casewhere the at-hand procedure does concern gingival recess.

As one example, with respect to a first indication the processing logicmay commence by assigning an importance rank of zero to the indication,determine that consideration of a first weighting factor findsindication that the indication's rank be raised by three, thatconsideration of a second weighting factor finds indication that theindication's rank be lowered by one, and that consideration of a thirdweighting factor finds indication that the indication's rank be raisedby five. The processing logic may then ascertain the finalizedimportance rank of the first indication to be seven.

Further, with respect to a second indication the processing logic maycommence by assigning an importance rank of zero to the indication,determine that consideration of a first weighting factor findsindication that the indication's rank be lowered by two, thatconsideration of a second weighting factor finds indication that theindication's rank be lowered by three, and that consideration of a thirdweighting factor finds indication that the indication's rank be raisedby six. The processing logic may then ascertain the finalized importancerank of the second indication to be one.

With respect to a third indication the processing logic may againcommence by assigning an importance rank of zero. The processing logicmay then find that consideration of a first weighting factor findsindication that the indication's rank be raised by four, thatconsideration of a second weighting factor finds indication that theindication be considered to possess a zenith rank, and thatconsideration of a third weighting factor finds indication that theindication's rank be lowered by eight. The processing logic may thenascertain the finalized importance rank of the third indication to bethe zenith rank. As such, the second weighting factor, by indicatingzenith rank, might be seen as having trumped that which was indicated bythe other two weighting factors. It is noted that had consideration ofthe second weighting factor instead found indication that the indicationbe considered to possess a nadir rank, the second weighting factor wouldhave again trumped the other two weighting factors, but would have doneso in a fashion that yielded a finalized importance rank of the nadirrank for the third indication.

At block 350, the processing logic may ascertain a finalized importancerank for each of the one or more under-consideration indications. Atblock 355, the processing logic may consider relative to one another thefinalized importance ranks of the one or more under-considerationindications. Continuing the above example, the processing logic mayconsider the three finalized importance ranks—seven for the firstindication, one for the second indication, and zenith rank for the thirdindication—relative to one another. In so doing the processing logic mayconclude the third indication to be highest-ranked, the first indicationto be second-highest-ranked, and the second indication to belowest-ranked.

At block 360, the processing logic may employ the finalized importanceranks in formulating a rescan order and/or a practitioner attentionorder. The processing logic may employ the importance ranks ofindications in suggesting a rescan order for one or more indications(e.g., indications regarding scan assistance such as indicationsconcerning missing and/or flawed scan data) and/or in suggesting apractitioner attention order for one or more indications (e.g.,indications regarding diagnostic assistance and/or indications regardingforeign object recognition assistance). In formulating such rescan orderand such practitioner attention orders the processing logic may or maynot suppress one or more indications such that those indications areexcluded from the rescan order or practitioner attention order. As oneillustration, the processing logic may suppress indications having ranklower than a certain value (e.g., a value specified by the user and/orduring a configuration). As another illustration, the processing logicmay suppress indications having the nadir rank. Such suppression mayserve to eliminate indications determined by the processing logic tolack clinical significance (e.g., with respect to an at-handprocedure—say preparation for application of a crown or an orthoticalignment device). As an illustration, suppressed indications mayinclude missing and/or flawed scan data for which compensation can beperformed (e.g., via the employ of extrapolation and/or generic datafilling). The processing logic may—for those indications which have notbeen suppressed—convey importance rank via keying of indications (e.g.,via color, symbol, icon, size, text, and/or number key).

Then, at block 365 the processing logic may provide in connection with adepiction of teeth and/or gingivae (e.g., a 3D image or a virtual 3Dmodel) one or more keyed indications (e.g., flags) conveying for eachindication its location in the rescan order and/or in the practitionerattention order. The processing logic may provide flags includingnumerals which each point to a particular portion of a depiction (e.g.,a 3D image or a virtual 3D model) of teeth and/or gingivae of a patientand convey via the numeral the order-wise location of that oral portion.

As an illustration, suppose that there are four indications regardingscan assistance selectable by the processing logic for inclusion in arescan order—an indication corresponding to teeth 15 and 16 (ISO 3950notion), an indication corresponding to the gingiva of tooth 32 (ISO3950 notation), an indication corresponding to teeth 18 and 17 (ISO 3950notation), and an indication corresponding to tooth 44 (ISO 3950notation). Then suppose that the indication correspond to teeth 18 and17 has the nadir rank, and that the processing logic suppresses thisindication, thereby eliminating it from the rescan order. Supposefurther that the rescan order for the three remaining indications issuch that the indication corresponding to the gingiva of tooth 32 hasthe highest importance rank of the remaining three and is first in therescan order, that the indication corresponding to teeth 15 and 16 hasthe second highest importance rank of the remaining three and is secondin the rescan order, and that the indication corresponding to tooth 44has the lowest importance rank of the remaining three and is third inthe rescan order. The provision of flags by the processing logic may besuch that the indication corresponding to the gingiva of tooth 32 isflagged with a “1,” the indication corresponding to teeth 15 and 16 isflagged with a “2,” and the indication corresponding to tooth 48 isflagged with a “3.”

As another illustration, suppose that there are three indicationsregarding diagnostic assistance—an indication corresponding to breakageof teeth 11 and 21 (ISO 3950 notation), an indication corresponding tobite relation, and an indication corresponding to gingival recess at thebase of tooth 27 (ISO 3950 notation). Suppose further that thepractitioner attention order is such that the indication correspondingto bite relation has the highest importance rank of the three and isfirst in the practitioner attention order, that the indicationcorresponding to the breakage has the second highest importance rank ofthe three and is second in the practitioner attention order, and thatthe indication corresponding to the gingival recess has the lowestimportance rank of the three and is third in the practitioner attentionorder. The provision of flags by the processing logic may be such thatthe indication corresponding to bite relation is flagged with a “1,” theindication corresponding to the breakage is flagged with a “2,” and theindication corresponding to the gingival recess is flagged with a “3.”

As an additional illustration, suppose that there are two indicationsregarding foreign object recognition assistance—an indicationcorresponding to a filling in tooth 16 (ISO 3950 notation) and anindication corresponding to a bridge at the expected anatomical locationof teeth 35-37 (ISO 3950 notation). Suppose further that thepractitioner attention order is such that the indication correspondingto the filling has the higher importance rank of the two and is first inthe practitioner attention order, and that the indication correspondingto the bridge has the lower importance rank of the two and is second inthe practitioner attention order. The provision of flags by theprocessing logic may be such that the indication corresponding to thefilling is flagged with a “1” and the indication corresponding to thebridge is flagged with a “2.”

FIG. 3C illustrates a flow diagram for a method 370 of employing 3Dintraoral images in providing indication of intraoral areas of interest,in accordance with examples of the present invention. As discussedhereinabove, the processing logic may provide indication regarding scanassistance, diagnostic assistance, and/or foreign object recognitionassistance. As also discussed hereinabove, the processing logic mayprovide such indication during the user's (e.g., the practitioner's)scanner application, after the user's scanner application, and/or priorto and/or without construction of an intraoral virtual 3D model. Asadditionally discussed hereinabove, in formulating such indication theprocessing logic may analyze intraoral scan data (e.g., 3D intraoralimages, say 3D intraoral images provided by the scanner as 3D imagepoint clouds) and/or intraoral virtual 3D models.

At block 372 of method 370 the processing logic may analyze one or morefirst 3D intraoral images to yield a candidate intraoral area ofinterest. The processing logic may perform such analysis as discussedhereinabove with respect to AOI formulation, but consider the analysisresult to constitute a candidate intraoral area of interest rather thanan actual intraoral area of interest. The processing logic may identifyone or more points (e.g., one or more pixels and/or groups of pixels)corresponding to the candidate intraoral area of interest.

At block 374, the processing logic may identify one or more second 3Dintraoral images which may be relevant to the candidate intraoral areaof interest. The one or more second 3D intraoral images may be oneswhich are intraorally proximal to the first one or more 3D intraoralimages and/or ones which share geometrical relation to the first one ormore 3D intraoral images. The processing logic may determine suchintraoral proximity by considering intraoral location informationprovided by the scanner in connection with 3D intraoral images. Thescanner might produce such information by way of incorporatedaccelerometers and/or other positioning hardware. The processing logicmay determine such shared geometrical relation by identifying commonsurface features (e.g., common peak and/or valley surface features).

At block 376, the processing logic may perform analysis with respect toone or more of the first 3D intraoral images and the second 3D intraoralimages taken together. In so doing the processing logic may or may notalign the one or more first 3D intraoral images with the one or moresecond 3D intraoral images (e.g., the processing logic may align one ormore point clouds corresponding to the first one or more 3D intraoralimages with one or more point clouds corresponding to the second one ormore 3D intraoral images).

At block 378, the processing logic may determine whether the first 3Dintraoral images and the second 3D intraoral images, taken together,agree, disagree, or agree in part with the candidate intraoral area ofinterest. As example, suppose that the candidate indication regards ascan assistance indication concerning missing and/or flawed scan data.In the case where such taken-together analysis finds no missing and/orflawed scan data disagreement may occur. As an illustration, such mayoccur where all of the one or more points (e.g., one or more pixelsand/or groups of pixels) which correspond to missing scan data for thecandidate indication are provided by the one or more second 3D intraoralimages.

In the case where such taken-together analysis still finds missingand/or flawed scan data but the tenor of the missing and/or flawed scandata changes (e.g., the now-found missing and/or flawed scan data is ofa larger size, of a smaller size, of a different location, and/or of adifferent morphology), partial agreement may occur. As an illustration,such might occur where some points (e.g., one or more pixels and/orgroups of pixels) which correspond to missing scan data for thecandidate indication are provided by the one or more second 3D intraoralimages while other of such missing scan data points are not provided bythe one or more second 3D intraoral images such that a smaller amount ofmissing and/or flawed scan data is found.

In the case where such taken-together analysis finds the same tenor(e.g., the same amount of) missing and/or flawed scan data, agreementmay occur. As an illustration, such might occur where none of the points(e.g., one or more pixels and/or groups of pixels) which correspond tomissing scan data for the candidate indication are provided by the oneor more second 3D intraoral images.

As another example, suppose that the candidate indication regards adiagnostic assistance indication concerning caries. In the case wheresuch taken-together analysis no longer finds caries disagreement mayoccur. As an illustration, such may occur where further taking intoaccount the one or more second 3D intraoral images—say further takinginto account one or more points (e.g., one or more pixels and/or groupsof pixels) provided by the one or more second 3D intraoral images—yieldsa refined intraoral vantage point from which caries are no longer found.

In the case where such taken-together analysis still finds caries butthe tenor of the found caries changes (e.g., the now-found caries aresmaller, larger, of different location, and/or of different morphology),partial agreement may occur. As an illustration, such might occur wherefurther taking into account the one or more second 3D intraoralimages—say further taking into account one or more points (e.g., one ormore pixels and/or groups of pixels) provided by the one or more second3D intraoral images—yields a refined intraoral vantage point from whichthe found caries differ in size and/or intraoral location.

In the case where such taken-together analysis finds caries of the sametenor as that found in connection with the candidate indicationagreement may occur. As an illustration, such might occur where furthertaking into account the one or more second 3D intraoral images—sayfurther taking into account one or more points (e.g., one or more pixelsand/or groups of pixels) provided by the one or more second 3D intraoralimages—yields does not refine the intraoral vantage point in a way thatcauses the found caries to differ in size and/or in intraoral location.

Where the processing logic finds agreement, the processing logic maypromote the candidate AOI to an indication of the sort discussedhereinabove (i.e., a full, non-candidate AOI) and employ it as discussedhereinabove (e.g., provide an indication of the AOI to a user in theform of flags). Where the processing logic finds partial agreement, theprocessing logic may yield an AOI corresponding to the above-discusseddifferent tenor (e.g., an AOI reflecting a smaller amount of missingdata or an AOI reflecting a caries of different morphology) and employit as discussed hereinabove. Where the processing logic findsdisagreement, the processing logic may reject the candidate AOI.

Where there is agreement the processing logic may proceed to block 380,in which the processing logic promotes the candidate indication to afull, non-candidate indication and employs the promoted indication asdiscussed above. Where there is partial agreement the processing logicmay proceed to block 382, in which the processing logic yields anindication corresponding to the tenor of the partial agreement, andemploys that indication as discussed above. Where there is disagreementthe processing logic may proceed to block 384, in which the processingrejects the candidate indication.

Previously discussed pooled patient data and/or pedagogical patient datamay include many different types of data and/or depictions. Someexamples of different pooled patient data and/or pedagogical patientdata and its use is now discussed.

Pooled patient data and/or pedagogical patient data may includedepictions of gum lines, bites, and/or bite lines, along withcorresponding identifications thereof and/or clarity level indicationsthereof. Indication regarding unclear gum line and/or unclear patientbite may involve the processing logic employing pooled patient dataand/or pedagogical patient data to recognize that patient scan dataand/or virtual 3D models includes a gum line or bite that is unclearlyimaged (e.g., deviates, in a fashion suggestive of unclarity, from gumline or bite indicated by the pooled and/or pedagogical data to possessclarity.

Pooled patient data and/or pedagogical patient data may additionallyinclude depictions of margin lines, tooth stumps, and/or accumulations(e.g., blood and/or saliva accumulations) along with correspondingidentifications thereof. Indication regarding unclear margin line mayinvolve the processing logic employing pooled patient data and/orpedagogical patient data to recognize that patient scan data and/or avirtual 3D model constitute a margin line (e.g., an upper portion of atooth stump which is to receive prosthetic crown). Additionally, oralternatively, the processing logic may compare the margin line of underconsideration patient scan data and/or one or more virtual 3D models ofthe patient to the margin line of an earlier-in-the-visit and/or dentalrecord data to detect a margin line change suggestive of the buildup ofblood, saliva, and/or like accumulation on the margin. The processinglogic may consider found margin lines together with found blood, saliva,and/or like accumulation to locate instances of such accumulationappearing in the vicinity of such margin lines, and to conclude suchinstances to constitute unclear margin lines.

Pooled patient data and/or pedagogical patient data may includedepictions of incisor edges and/or of double incisor edges along withcorresponding identifications thereof. Indication regarding doubleincisor edge surface conflict may involve the processing logic employingpooled patient data and/or pedagogical patient data to recognize thatpatient scan data and/or virtual 3D models include one or more incisoredges, and further to conclude such incisor edges to deviate, in afashion suggestive of double incisor edge, from incisor edges indicatedby the pooled and/or pedagogical data to be proper incisor edges.

Pooled patient data and/or pedagogical patient data may includedepictions of tooth occlusion contacts and/or bite relations along withcorresponding identifications thereof. Indication regarding toothocclusion contacts and/or bite relation may involve the processing logicemploying pooled patient data and/or pedagogical patient data torecognize that patient scan data and/or virtual 3D models constitutetooth occlusion contacts and/or bite relation. The processing logic mayfurther access one or more treatment goals (e.g., a desired degree ofocclusion with respect to one or more indicated teeth and/or a desiredbite relation). Such goals may be provided by a practitioner (e.g., viaa user interface) and/or be retrieved from an accessible data store. Theprocessing logic may then compare the tooth occlusion contacts and/orbite relation of the under consideration patient scan data and/or one ormore virtual 3D models of the patient to the tooth occlusion contactsand/or bite relation of earlier-in-the-visit and/or dental record datato detect the degree of change (which might be null) of tooth occlusioncontacts and/or bite relation. The processing logic may then compare thedetermined change to a treatment goal and ascertain whether the changecauses satisfaction of the treatment goal, or whether the change servesto approach or depart the goal. The indication may include notificationas to whether or not the change approaches, departs, meets the treatmentgoal, or results in no change relative to the treatment goal.

As one illustration, the aforementioned regarding tooth occlusioncontacts may correspond to a circumstance in which a practitionerindicates a tooth occlusion contact treatment goal to the processinglogic, has the processing logic receive scan data depicting a startingocclusion contact state of the patient, performs a dental procedurewhich serves to potentially alter that occlusion contact state, and thathas the processing logic receive scan data depicting the post-procedureocclusion contact state. Via processing in vein of that which isdiscussed above, the practitioner may receive indication as to whetherhis procedure has met the treatment goal, caused progress towards thetreatment goal, caused departure from the treatment goal, or resulted inno change relative to the treatment goal.

As another illustration, the aforementioned regarding bite relation maycorrespond to a circumstance in which a practitioner indicates a biterelation treatment goal to the processing logic, has the processinglogic receive scan data depicting a starting bite relation state of thepatient, applies an orthotic alignment device to the patient, and hasthe patient return at a later data. Then, at the later date, thepractitioner has the processing logic receive scan data depicting thepost-device-application bite relation state. Via processing in vein ofthat which is discussed above, the practitioner may receive indicationas to whether his device application has caused the treatment goal to bemet, caused progress towards the treatment goal, caused departure fromthe treatment goal, or has resulted in no change relative to thetreatment goal.

Pooled patient data and/or pedagogical patient data may includedepictions of tooth breakage, tooth wear, gingival swelling, gingivalrecess, and/or caries along with corresponding identifications thereof.Indication regarding tooth breakage, tooth wear, gingival swelling,gingival recess, and/or caries may involve the processing logicemploying pooled patient data and/or pedagogical patient data torecognize that patient scan data and/or one or more virtual 3D modelsconstitute tooth breakage, tooth wear, gingival swelling, gingivalrecess, and/or caries. For example, the processing logic may employpooled patient data and/or pedagogical patient data to recognize teethand/or gingiva in intraoral images and/or virtual 3D models. Theprocessing logic may then compare the teeth and/or gingivae of theintraoral images and/or virtual 3D models to the teeth and/or gingivaeof earlier intraoral images, virtual 3D models and/or dental record datato detect change indicative of tooth breakage, tooth wear, gingivalswelling, gingival recess, and/or caries. In performing such detectionthe processing logic may or may not perform image analysis (e.g.,considering a discovered change to be indicative of tooth breakage inthe case where the change possesses a jagged edge) and/or consultpatient data and/or pedagogical patient data (e.g., considering adiscovered change to be indicative of tooth breakage in the case wherethe change matches one or more items indicated by the patient dataand/or pedagogical patient data to constitute breakage).

Indication concerning tooth breakage and/or caries may involve theprocessing logic performing direct analysis. The processing logic mayadditionally or alternatively employ pooled patient data and/orpedagogical patient data to recognize that the patient scan data and/orone or more virtual 3D models includes areas that constitute teeth. Theprocessing logic may determine (e.g., via edge recognition) one or moreof such teeth to possess one or more jagged edges. The processing logicmay consider such jagged edges to be indicative of tooth breakage. Theprocessing logic may determine (e.g., via shape recognition) one or moreof such teeth to possess spots and/or lacunae. The processing logic mayconsider such spots and/or lacunae to be indicative of caries.

The indications regarding foreign object recognition assistance providedby the processing logic may include indications concerning fillings,implants, and/or bridges. Pooled patient data and/or pedagogical patientdata may include depictions of fillings, implants, and/or bridges alongwith corresponding identifications thereof. Indication regardingfillings, implants, and/or bridges may involve the processing logicemploying pooled patient data and/or pedagogical patient data torecognize patient scan data and/or virtual 3D models which constitutefillings, implants, and/or bridges. Indication regarding fillings,implants, and/or bridges may involve the processing logic comparing theunder consideration patient scan data and/or one or more virtual 3Dmodels of the patient to earlier in the patient visit data, dentalrecord data of the patient, and/or data of the patient from prior to theat-hand patient visit. The processing logic may consider objects whichappear in the under consideration patient scan data and/or one or morevirtual 3D models of the patient but not in the earlier in the patientvisit data, dental record data of the patient, and/or data of thepatient from prior to the at-hand patient visit to be possible foreignobjects. Such functionality might, for instance, be implemented from theviewpoint that new objects appearing in a patient's mouth have a certainlikelihood of being foreign objects rather than naturally-occurringones. The processing logic may allow a practitioner to respond (e.g.,via user interface) to such an indication with agreement and/ordisagreement that processing logic-identified objects are foreignobjects.

FIG. 4A illustrates an example scanned portion of a dental arch 400during an intraoral scan session. The dental arch 400 includes gums 404and multiple teeth 410, 420. Multiple intraoral images 425, 430, 435,440 have been taken of a dental site of a patient. Each of the intraoralimages 425-440 may have been generated by an intraoral scanner having aparticular distance from the dental surface being imaged. At theparticular distance, the intraoral images 425-440 have a particular scanarea and scan depth. The shape and size of the scan area will generallydepend on the scanner, and is herein represented by a rectangle. Eachimage may have its own reference coordinate system and origin. Eachintraoral image may be generated by a scanner at a particular position(scanning station). The location and orientation of scanning stationsmay be selected such that together the intraoral images adequately coveran entire target zone. Preferably, scanning stations are selected suchthat there is overlap between the intraoral images 425-440 as shown.Typically, the selected scanning stations will differ when differentscanners are used for the same target area, depending on the capturecharacteristics of the scanner used. Thus, a scanner capable of scanninga larger dental area with each scan (e.g., having a larger field ofview) will use fewer scanning stations than a scanner that is onlycapable of capturing 3D data of a relatively smaller dental surface.Similarly, the number and disposition of scanning stations for a scannerhaving a rectangular scanning grid (and thus providing projectedscanning areas in the form of corresponding rectangles) will typicallybe different from those for a scanner having a circular or triangularscanning grid (which would provide projected scanning areas in the formof corresponding circles or triangles, respectively).

Intraoral areas of interest 448 and 447 have been computed as discussedherein above. In the illustrated embodiment, the intraoral areas ofinterest 447, 448 represent portions of the patient's dental site thatlack image data.

FIG. 4B illustrates a scanned portion of a dental arch 402 that is anupdate of dental arch 400. Additional intraoral images 458, 459 havebeen taken to provide image data corresponding to intraoral areas ofinterest 447, 448. Accordingly, intraoral areas of interest 447, 448 areno longer shown in dental arch 402. Additional intraoral images 460,462, 464, 466 have also been generated. These additional intraoralimages 460-466 reveal teeth 450, 452, 454, 456. New intraoral areas ofinterest 470, 472 are also determined based on the additional intraoralimages 460-466. A practitioner may generate still further intraoralimages to resolve intraoral areas of interest 470, 472 and to providedata for a full dental arch.

FIG. 5A illustrates an example image of a dental arch 500 showing areasof interest. The image of the dental arch 500 may be constructed fromone or more intraoral scans prior to generation of a virtual 3D model.Alternatively, the image of the dental arch 500 may be constructed fromone or more scans of a physical model of a dental arch. The image of thedental arch 500 includes gums 509 and multiple teeth 505-508. Multipleareas of interest 509, 515, 525 are also shown in the image of thedental arch 500. These areas of interest 509, 515, 525 represent missingscan data that satisfies a clinical importance criterion.

FIG. 5B illustrates an example image of a dental arch 550 showing areasof interest and indications pointing to the areas of interest. The imageof the dental arch 550 may be constructed from one or more intraoralscans. Alternatively, the image of the dental arch 550 may beconstructed from one or more scans of a physical model of a dental arch.The image of the dental arch 550 includes gums and multiple teeth.Multiple areas of interest 562, 564, 566, 568, 570, 572 are also shownin the image of the dental arch 550. These areas of interest 562, 564,566, 568, 570, 572 represent missing scan data that satisfies a clinicalimportance criterion (e.g., intraoral areas of interest greater than athreshold size or having one or more dimensions that violate a geometriccriterion). However, some areas of interest 562, 570 are largelyoccluded in the example image of the dental arch 550. Additionally,there are other areas of interest that are completely hidden. To ensurethat a dental practitioner is made aware of such areas of interest, anindicator such as a flag is presented for each area of interest. Forexample, the image of the dental arch 550 includes flags 552-559. Theseflags call the dental practitioners attention to areas of interest thatshould be addressed regardless of a present view.

FIG. 5C illustrates another example image of a dental arch 575 showingareas of interest and indications pointing to the areas of interest. Theimage of the dental arch 575 may be constructed from one or moreintraoral scans. Alternatively, the image of the dental arch 575 may beconstructed from one or more scans of a physical model of a dental arch.The image of the dental arch 575 includes gums and multiple teeth.Multiple areas of interest 576-584 are also shown in the image of thedental arch 575. These areas of interest 576-584 represent tooth wearthat is identified based on a comparison between images and/or a virtual3D model generated at a first date and images and/or a virtual 3D modelgenerated at a second date. However, some areas of interest 576, 578 arelargely occluded in the example image of the dental arch 575. To ensurethat a dental practitioner is made aware of such areas of interest, anindicator such as a flag is presented for each area of interest. Forexample, the image of the dental arch 575 includes flags 586-594. Theseflags call the dental practitioners attention to areas of interest thatshould be addressed regardless of a present view.

FIG. 6 illustrates a screen shot 600 of an intraoral scan application(e.g., of intraoral scan application 108 of FIG. 1 ), in accordance withembodiments of the present invention. The screen shot 600 shows multiplemenus 602, 604, 606 for performing various operations. Menu 602 providesicons that can be selected to perform global operations such as changingsettings, saving data, obtaining assistance, generating a virtual 3Dmodel from gathered intraoral images, switching to a view mode, and soforth. Menu 604 provides icons for adjusting a view 607 of a scanneddental site 608. Menu 604 may include icons for panning, zooming,rotating, and so forth. The view 607 of the scanned dental site 608includes a dental arch made up of one or more previous intraoral imagesthat have been registered and/aligned with one another. The view 607further includes an indication of a latest intraoral image 610 that hasbeen added to the dental arch.

The dental ach includes multiple voids based on incomplete scan data.Such voids are one type of intraoral area of interest that is called outby flags 612-624. Menu 606 includes scanning instructions that enable auser to proceed to a next scan, redo a last scan, rescan a segment, andso on. A user may rescan one or more segments to provide scan data thatcan fill in the voids that are called out by flags 612-624. This canensure that a final virtual 3D model that is generated based on theintraoral images is of high quality.

FIG. 7 illustrates a diagrammatic representation of a machine in theexample form of a computing device 700 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein, may be executed. In alternativeembodiments, the machine may be connected (e.g., networked) to othermachines in a Local Area Network (LAN), an intranet, an extranet, or theInternet. The machine may operate in the capacity of a server or aclient machine in a client-server network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a tablet computer, a set-topbox (STB), a Personal Digital Assistant (PDA), a cellular telephone, aweb appliance, a server, a network router, switch or bridge, or anymachine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines (e.g., computers)that individually or jointly execute a set (or multiple sets) ofinstructions to perform any one or more of the methodologies discussedherein.

The example computing device 700 includes a processing device 702, amain memory 704 (e.g., read-only memory (ROM), flash memory, dynamicrandom access memory (DRAM) such as synchronous DRAM (SDRAM), etc.), astatic memory 706 (e.g., flash memory, static random access memory(SRAM), etc.), and a secondary memory (e.g., a data storage device 728),which communicate with each other via a bus 708.

Processing device 702 represents one or more general-purpose processorssuch as a microprocessor, central processing unit, or the like. Moreparticularly, the processing device 702 may be a complex instruction setcomputing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction word (VLIW) microprocessor,processor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processing device 702may also be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. Processing device 702 is configured to execute theprocessing logic (instructions 726) for performing operations and stepsdiscussed herein.

The computing device 700 may further include a network interface device722 for communicating with a network 764. The computing device 700 alsomay include a video display unit 710 (e.g., a liquid crystal display(LCD) or a cathode ray tube (CRT)), an alphanumeric input device 712(e.g., a keyboard), a cursor control device 714 (e.g., a mouse), and asignal generation device 720 (e.g., a speaker).

The data storage device 728 may include a machine-readable storagemedium (or more specifically a non-transitory computer-readable storagemedium) 724 on which is stored one or more sets of instructions 726embodying any one or more of the methodologies or functions describedherein. A non-transitory storage medium refers to a storage medium otherthan a carrier wave. The instructions 726 may also reside, completely orat least partially, within the main memory 704 and/or within theprocessing device 702 during execution thereof by the computer device700, the main memory 704 and the processing device 702 also constitutingcomputer-readable storage media.

The computer-readable storage medium 724 may also be used to store anintraoral scan application 750, which may correspond to the similarlynamed component of FIG. 1 . The computer readable storage medium 724 mayalso store a software library containing methods for an intraoral scanapplication 750. While the computer-readable storage medium 724 is shownin an example embodiment to be a single medium, the term“computer-readable storage medium” should be taken to include a singlemedium or multiple media (e.g., a centralized or distributed database,and/or associated caches and servers) that store the one or more sets ofinstructions. The term “computer-readable storage medium” shall also betaken to include any medium other than a carrier wave that is capable ofstoring or encoding a set of instructions for execution by the machineand that cause the machine to perform any one or more of themethodologies of the present invention. The term “computer-readablestorage medium” shall accordingly be taken to include, but not belimited to, solid-state memories, and optical and magnetic media.

It is to be understood that the above description is intended to beillustrative, and not restrictive. Many other embodiments will beapparent upon reading and understanding the above description. Althoughembodiments of the present invention have been described with referenceto specific example embodiments, it will be recognized that theinvention is not limited to the embodiments described, but can bepracticed with modification and alteration within the spirit and scopeof the appended claims. Accordingly, the specification and drawings areto be regarded in an illustrative sense rather than a restrictive sense.The scope of the invention should, therefore, be determined withreference to the appended claims, along with the full scope ofequivalents to which such claims are entitled.

What is claimed is:
 1. An intraoral scanning system, comprising: anintraoral scanner to generate a plurality of two-dimensional (2D) imagesof a dental site and a plurality of three-dimensional (3D) intraoralscans of the dental site; and a computing device to: receive theplurality of 2D images of the dental site and the plurality of 3Dintraoral scans of the dental site from the intraoral scanner; generatea 3D model of the dental site based on the plurality of 3D intraoralscans of the dental site; process at least one of a) one or more of theplurality of 2D images of the dental site, b) one or more of theplurality of 3D intraoral scans of the dental site, or c) data from the3D model of the dental site to identify one or more intraoral areas ofinterest (AOIs) at the dental site; determine a dental conditionassociated with the one or more intraoral AOIs; and determine a mannerfor scanning the one or more intraoral AOIs.
 2. The intraoral scanningsystem of claim 1, wherein the determining the optimal manner forscanning the one or more intraoral AOIs is performed based at least inpart on the determined dental condition.
 3. The intraoral scanningsystem of claim 1, wherein the intraoral scanner is further to rescanthe one or more intraoral AOIs in accordance with the determined optimalmanner for scanning the one or more intraoral AOIs.
 4. The intraoralscanning system of claim 3, wherein the computing device is further to:receive a second plurality of 2D images of the dental site and a secondplurality of 3D intraoral scans of the dental site generated from therescan of the one or more intraoral AOIs; and update the 3D model of thedental site based on the second plurality of 3D intraoral scans of thedental site.
 5. The intraoral scanning system of claim 1, wherein thedental condition is selected from a group consisting of: caries, abroken tooth, gum disease, a lesion, receding gums, or tooth wear. 6.The intraoral scanning system of claim 1, wherein determining theoptimal manner for scanning the one or more intraoral AOIs comprisesdetermining a rescan order for scanning the one or more intraoral AOIs.7. The intraoral scanning system of claim 1, wherein the plurality of 3Dintraoral scans comprise monochrome scans and the plurality of 2D imagescomprise color images.
 8. The intraoral scanning system of claim 1,further comprising: a display, wherein the computing device is to outputthe 3D model of the dental site and an indication of the one or moreintraoral AOIs and associated dental condition to the display.
 9. Theintraoral scanning system of claim 1, wherein the computing device isfurther to: notify a user of the intraoral scanner that the one or moreintraoral AOIs should be rescanned.
 10. The intraoral scanning system ofclaim 1, wherein the computing device applies machine learning toprocess at least one of a) the one or more of the plurality of 2D imagesof the dental site, b) the one or more of the plurality of 3D intraoralscans of the dental site, or c) the data from the 3D model of the dentalsite to identify the one or more intraoral areas of interest (AOIs) atthe dental site and to determine the dental condition associated withthe one or more intraoral AOIs.
 11. An intraoral scanning system,comprising: an intraoral scanner to generate a plurality oftwo-dimensional (2D) images of a dental site and a plurality ofthree-dimensional (3D) intraoral scans of the dental site; and acomputing device to: receive the plurality of 2D images of the dentalsite and the plurality of 3D intraoral scans of the dental site from theintraoral scanner; generate a 3D model of the dental site based on theplurality of 3D intraoral scans of the dental site; process at least oneof a) one or more of the plurality of 2D images of the dental site, b)one or more of the plurality of 3D intraoral scans of the dental site,or c) data from the 3D model of the dental site to identify one or moreintraoral areas of interest (AOIs) at the dental site; classify the oneor more intraoral AOIs as caries, a broken tooth, gum disease, a lesion,receding gums, or tooth wear; and determine how to rescan the one ormore intraoral AOIs.
 12. The intraoral scanning system of claim 11,further comprising: a display, wherein the computing device is to outputthe 3D model of the dental site and an indication of the one or moreintraoral AOIs to the display.
 13. The intraoral scanning system ofclaim 11, wherein how to rescan the one or more AOIs is determined basedon at least one of a classification of the one or more AOIs, a size ofthe one or more AOIs, or a severity of the one or more AOIs.
 14. Theintraoral scanning system of claim 11, wherein the computing device isfurther to: receive a second plurality of 2D images of the dental siteand a second plurality of 3D intraoral scans of the dental sitegenerated responsive to a rescan of the one or more intraoral AOIs bythe intraoral scanner; and update the 3D model of the dental site basedon the second plurality of 3D intraoral scans of the dental site. 15.The intraoral scanning system of claim 14, wherein the plurality oftwo-dimensional (2D) images of a dental site, the plurality ofthree-dimensional (3D) intraoral scans of the dental site, the secondplurality of 2D images of the dental site, and the second plurality of3D intraoral scans of the dental site are generated during a singleintraoral scanning session.
 16. The intraoral scanning system of claim11, wherein the plurality of 3D intraoral scans comprise monochromescans and the plurality of 2D images comprise color images.
 17. Theintraoral scanning system of claim 11, wherein the computing device isfurther to: notify a user of the intraoral scanner that the one or moreAOIs should be rescanned.
 18. The intraoral scanning system of claim 11,wherein determining how to rescan the one or more intraoral AOIscomprises determining an optimal manner for scanning the one or moreAOIs.
 19. The intraoral scanning system of claim 11, wherein thecomputing device applies machine learning to process at least one of a)the one or more of the plurality of 2D images of the dental site, b) theone or more of the plurality of 3D intraoral scans of the dental site,or c) the data from the 3D model of the dental site to identify the oneor more intraoral areas of interest (AOIs) at the dental site and toclassify the one or more intraoral AOIs as caries, a broken tooth, gumdisease, a lesion, receding gums, or tooth wear.
 20. The intraoralscanning system of claim 11, wherein determining how to rescan the oneor more AOIs comprises determining a rescan order for scanning the oneor more intraoral AOIs.
 21. An intraoral scanning system, comprising: anintraoral scanner to generate a plurality of two-dimensional (2D) imagesof a dental site and a plurality of three-dimensional (3D) intraoralscans of the dental site; and a computing device to: receive theplurality of 2D images of the dental site and the plurality of 3Dintraoral scans of the dental site from the intraoral scanner; generatea 3D model of the dental site based on the plurality of 3D intraoralscans of the dental site; process at least one of a) one or more of theplurality of 2D images of the dental site, b) one or more of theplurality of 3D intraoral scans of the dental site, or c) data from the3D model of the dental site to identify an intraoral area of interest(AOI) at the dental site; determine a dental condition associated withthe intraoral AOI; and determine one or more scanning parameters forrescanning the intraoral AOI based on the determined dental condition.22. The intraoral scanning system of claim 21, wherein the dentalcondition is selected from a group consisting of: caries, a brokentooth, gum disease, a lesion, receding gums, or tooth wear.
 23. Theintraoral scanning system of claim 21, wherein the one or more scanningparameters comprise a rescan order for scanning the one or moreintraoral AOIs.